LncRNA CACNA1G-AS1 up-regulates FTH1 to inhibit ferroptosis and promote malignant phenotypes in ovarian cancer cells

Previous study revealed that ferritin heavy chain-1(FTH1)could regulate ferritinophagy and affect intracellular Fe^(+)content in various tumors,while its N6-methyladenosine(m6A)RNA methylation was closely related the prognosis of ovarian cancer patients.However,little is known about the role of FTH1 m6A methylation in ovarian cancer(OC)and its possible action mechanisms.In this study we constructed FTH1 m6A methylation regulatory pathway(LncRNA CACNA1G-AS1/IGF2BP1)according to related bioinformatics analysis and research,through clinical sample detections we found that these pathway regulatory factors were significantly up-regulated in ovarian cancer tissues,and their expression levels were closely related to the malignant phenotype of ovarian cancer.In vitro cell experiments showed that LncRNA CACNA1G-AS1 could up-regulate FTH1 expression through IGF2BP1 axis,thus inhibited ferroptosis by regulating ferritinophagy,and finally promoted proliferation and migration in ovarian cancer cells.Tumor-bearing mice studies showed that the knock-down of LncRNA CACNA1G-AS1 could inhibited the tumorigenesis of ovarian cancer cells in vivo condition.Our results demonstrated that LncRNA CACNA1G-AS1 could promote the malignant phenotypes of ovarian cancer cells through FTH1-IGF2BP1 regulated ferroptosis.

Effect of nitrogen fertilizer on distribution of starch granules in different regions of wheat endosperm

This study provided visual evidence of a nitrogen effect on starch granules(SGs) in wheat endosperm. Winter wheat(Titicum aestivum L.) cultivar Xumai 30 was cultured under no nitrogen(control) and 240 kg ha-1of nitrogen applied at the booting stage. The number, morphology, and size of Aand B-type SGs in subaleurone of dorsal endosperm(SDE), center of dorsal endosperm(CDE), modified aleurone(MA), subaleurone of ventral endosperm(SVE), and center of ventral endosperm(CVE) were observed under light and electron microscopes.(1) The distribution of SGs in SDE was similar to that in SVE, the distributions of SGs in CDE and CVE were similar, but the distribution of SGs in MA was different from those in the other four endosperm regions. The number of SGs in the five endosperm regions was in the order SDE > CDE > SVE > CVE > MA.(2) Nitrogen increased the number of Aand B-type SGs in SDE and SVE. Nitrogen also increased the number of B-type SGs but decreased the number of A-type SGs in CDE and CVE. Nitrogen decreased the numbers of A-type and B-type SGs in MA. The results suggest that increased N fertilizer application mainly increased the numbers of small SGs and decreased the numbers of large SGs, but that the results varied in different regions of the wheat endosperm.

E3GCAPS: Efficient EEG-Based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection

Cognitive state detection using electroencephalogram(EEG)signals for various tasks has attracted significant research attention.However,it is difficult to further improve the performance of crosssubject cognitive state detection.Further,most of the existing deep learning models will degrade significantly when limited training samples are given,and the feature hierarchical relationships are ignored.To address the above challenges,we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection,termed as Efficient EEG-based Multi-Capsule Framework(E3GCAPS).Specifically,we use a selfexpression module to capture the potential connections between samples,which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG.In addition,considering the strong correlation between cognitive states and brain function connection mode,the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data,in which multichannel 1D data greatly improving the training efficiency while preserving the model performance.The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset(FAAD)and the SJTU Emotion EEG Dataset(SEED).Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.